Informativeness is a determinant of compound stress in English1
Melanie J. Bell and Ingo Plag
January 2012
Abstract
There have been claims in the literature that the variability of compound stress assignment in English
can be explained with reference to the informativeness of the constituents (e.g. Bolinger 1972, Ladd
1984). Until now, however, large-scale empirical evidence for this idea has been lacking. This paper
addresses this deficit by investigating a large number of compounds taken from the British National
Corpus. It is the first study of compound stress variability in English to show that measures of
informativeness (the morphological family sizes of the constituents and the constituents’ degree of
semantic specificity) are indeed highly predictive of prominence placement. Using these variables as
predictors, in conjunction with other factors believed to be relevant (cf. Plag et al. 2008), we build a
probabilistic model that can successfully assign prominence to a given construction. Our finding, that
the more informative constituent of a compound tends to be most prominent, fits with the general
propensity of speakers to accentuate important information, and can therefore be interpreted as
evidence for an accentual theory of compound stress.
1
The authors wish to thank Sabine Arndt-Lappe, Kristina Kösling, Gero Kunter and the reviewers of
this journal for their feedback on earlier versions. Special thanks also to Harald Baayen for discussion
and support. This work was made possible by an AHRC postgraduate award (114200) and a major
studentship from Newnham College, Cambridge, to the first author as well as two grants from the
Deutsche Forschungsgemeinschaft (PL151/5-1, PL 151/5-3) to the second author, all of which are
gratefully acknowledged.
2
1. Introduction
An idiosyncratic aspect of Present-day English is that, while many noun-noun (henceforth
NN) combinations are pronounced with the left-stress pattern characteristic of Germanic
compounds, there are also many combinations that normally have right prominence. Some
examples are given in (1a) and (1b), where capital letters indicate the syllable that is usually
most prominent:
(1)
a.
TAble lamp
CREdit card
TEA cup
b.
silk SHIRT
christmas DAY
kitchen SINK
Most native speakers of English would produce the types in (1a) with main stress on the first
element but the types in (1b) with main stress on the second element. Of course, this can
change in a contrastive context. In they’re coming on CHRISTmas day, not BOXing day, the
compound christmas day, normally stressed on the second element, receives contrastive stress
on the first element. However, in this paper we are concerned not with contrastive contexts,
but with the characteristic prominence patterns of NN combinations spoken in a neutral
context or in their citation form.
A note on terminology is in order here. The terms used in studies of compound
prominence patterns are somewhat confused. Some scholars prefer the term ‘stress’, others
speak of ‘(prosodic) prominence’. The particular choice of terminology is often dependent on
the authors’ theoretical assumptions about the phenomenon. For example, the term
‘prominence’ seems to be used by people favouring an analysis of compound stress in terms
of pitch accents, instead of lexical stresses. Since there is no theory-neutral term available, we
use both terms more or less interchangeably in this paper.
Despite the issue of compound stress assignment having received considerable
attention from scholars of English over more than a hundred years, there is still no fully
3
satisfactory explanation of the facts and no completely successful way of predicting which
prominence pattern will apply to any given combination of nouns. This paper uses data from
the British National Corpus (BNC) and from a large-scale production experiment to
investigate a particular hypothesis about compound stress assignment. The hypothesis rests
on two assumptions. Our first assumption, based on acoustic studies of compound stress
(Farnetani et al. 1988; Plag 2006; Kunter & Plag 2007; Kunter 2011), is that different
compound prominence patterns are realised by differences in the distribution of pitch
accents: in left-prominent compounds, only the first constituent is accented, while in rightprominent compounds, both constituents are accented. Our second assumption is that, in
general in language, uninformative elements tend to be unaccented, while more informative
and unexpected information is accented. On the basis of these assumptions, we hypothesise
that a compound’s stress pattern is at least partly determined by the informativeness of its
second constituent. The hypothesis predicts that an uninformative constituent in the righthand position will not receive an accent, i.e. the compound will be left-stressed. On the other
hand, a highly informative constituent in the right-hand position will receive an accent, i.e.
the compound will be right-stressed (see discussion of phonetically-grounded studies in
section 2.1).
We investigate a number of measures of informativeness. By combining these with
other established determinants of compound stress, we are able to construct a probabilistic
model that achieves a higher rate of success in predicting NN prominence than any method
previously proposed in the literature. Overall, our study provides strong empirical evidence
for the important role of informativeness in compound stress assignment.
The paper is organised as follows: section 2 outlines previous attempts to explain the
variation in NN prominence, section 3 describes the methodology used in the present study,
sections 4 and 5 describe and discuss the results of our analyses, and section 6 summarises
the findings and discusses their implications for theories of compound stress.
4
2. The issues
2.1 General background
Throughout the twentieth century, in keeping with the prevalent linguistic paradigms of the
time, linguists sought rules by which to assign prominence to English NN combinations. One
of the most influential proposals was that of Chomsky & Halle (1968: 15-18), who coined the
‘Compound Rule’ and the ‘Nuclear Stress Rule’. The ‘Nuclear Stress Rule’ was said to
account for the normal prominence pattern in English phrases, in which the last strong
syllable is the most prominent, i.e. carries the nuclear stress. On the other hand, the
‘Compound Rule’ was taken to assign primary stress to the main-stressed vowel of the first
element of a binomial compound. Taken at face value, this suggests that NN combinations
with prominence on the first element are compounds, whereas those with prominence on the
second element are phrases. However, Chomsky & Halle (ibid.) make no attempt to define
by other criteria the strings to which each of these rules applies. This means that, if taken
literally, the rules are circular: left stress is assigned to compounds and compounds are
defined as those combinations that receive left stress. Obviously, such a rule is unworkable,
and in fact very few authors have taken it literally; Chomsky & Halle themselves (ibid.: 156)
point out that there are several kinds of exception to the Compound Rule and that there is a
need for ‘an investigation of the conditions, syntactic and other, under which the Compound
Rule is applicable’. So, if some NN combinations are to be analysed as phrases, there are two
questions to answer: what are the criteria by which the two classes can be distinguished, and
why do some combinations that most scholars regard as compounds nevertheless have right
prominence?
In fact, as argued by Bauer (1998), Olsen (2000) and Bell (2005, 2011), there is very
little evidence for a class of phrasal NNs. In this paper we will therefore use the term
‘compound’ for all NN constructions. We will investigate sequences consisting of two, and
only two, adjacent nouns, in which one modifies the meaning of the other, or where together
5
they have a single meaning different from the meaning of either constituent individually.
However, proper names, such as Laurie Bauer, and constructions with an appositive modifier,
such as (my) sister Lillian, are excluded.
The question then becomes: under what circumstances do Present-day English NNcompounds receive left prominence and under what circumstances do they receive right
prominence? The problem for any account that tries to answer this question in terms of a rule
is that, in order to achieve coverage of the empirical facts, it becomes necessary to append a
significant list of exceptions: at least, that has been the case for all rules so far postulated. For
example, Fudge (1984: 144-146) states that the majority of English NN compounds are
‘initially-stressed’, i.e. left-prominent, and then lists seven classes of exception. The first six of
these involve identifiable semantic categories, and might therefore be regarded as rulegoverned, but the final class, labelled ‘miscellaneous cases’, is not susceptible to such an
analysis. Furthermore, within his lists of exceptions, Fudge (ibid.: 144) asterisks exceptions to
the exceptions, where ‘initial stress is an alternative possibility’. Another example of a rulebased approach is that of Giegerich (2004), who suggests that the basic distinction between
left and right-prominent compounds is that in left-prominent types the first noun (N1) is a
complement of the second noun (N2), as in OPera singer, whereas in right-prominent types
N1 is an attribute of N2, as in steel BRIDGE. The problem for this hypothesis is that there are
many left-prominent combinations where N1 clearly takes the role of attribute rather than
complement (e.g. OPera glass), and Giegerich accounts for these exceptions by suggesting
that they are the product of a diachronic process of lexicalisation. Yet this implies that,
contrary to the facts, attribute-type compounds cannot be coined with left prominence: so
Giegerich, like Fudge, has to invoke exceptions to the exceptions. His solution is to argue
that, once certain attribute-type compounds become listed with left prominence through a
diachronic process of lexicalisation, others can be directly formed by analogy.
6
One of the reasons why categorical approaches struggle to account for the facts is that
compound prominence itself is not as categorical as a rule-based approach would suggest. It
is well known that some compounds vary with dialect, e.g. BOY scout in American English
but boy SCOUT in British English, and that others show free variation, e.g. ICE cream or ice
CREAM. Jespersen (1909: 155), for example, writes that ‘individual pronunciations vary not a
little on this point, and – what is very important - ... ‘level stress’ [i.e. right prominence] really
means ‘unstable equilibrium’’ (original emphasis). Other authors, e.g. Bauer (1978, 1983), Levi
(1978), Pennanen (1980) and Kunter (2010, 2011), have shown that prominence varies not
only between but also within speakers, both in production and perception. In addition to this
variation, there are cases where pairs of compounds with evidently similar structure and
semantics consistently receive contrary prominence patterns, depending on the identities of
their constituents, for example APPle cake and LEMon cake compared with apple PIE and
lemon PIE (see Sampson 1980 for an overview of the problems).
Apart from the difficulties caused by the complexities of the language itself, there are
also problems associated with the methodology used in most twentieth century studies. In
virtually all cases, authors used fairly small datasets, usually selected to illustrate specific
points, and assigned prominence to them on the basis of their own intuition, thus effectively
ruling out any opportunity to study the variation in prominence found in actual speech.
Many accounts in fact recycle the same small number of examples from previous papers.
Furthermore, as hinted in the preceding paragraphs, there is considerable variation in
terminology. ‘Uneven stress’ (Sweet 1892: 499), ‘unity stress’ (Jespersen 1909: 485), ‘one high
stress’ (Bloomfield 1935: 566), ‘compound stress’ (Chomsky & Halle 1968), ‘single stress’
(Jones 1972), ‘fronted stress’ (Levi 1978), ‘initial stress’ (Fudge 1984), ‘forestress’ (Zwicky
1986) and ‘lefthand stress’ (Liberman & Sproat 1992) are amongst the many terms used for
what we are calling ‘left prominence’. The respective terms for our ‘right prominence’ are
‘even stress’, ‘level stress’, ‘two high stresses’, ‘nuclear stress’, ‘double stress’, ‘normal
7
(nonfronted) stress’, ‘final stress’, ‘afterstress’ and ‘righthand stress’. It can be seen from the
variety of terms that there is some uncertainty about whether the relevant distinction is
between one and two stresses or between two possible positions of a single stress. In some
analyses three possibilities are admitted: stress on the first constituent only, stress on the
second constituent only, or stress on both.
Phonetically-grounded studies (Farnetani et al. 1988; Plag 2006; Kunter & Plag 2007;
Kunter 2011) have shown, however, that at the phonological level, only two patterns should
be assumed, which we call ‘left prominence’ and ‘right prominence’. Right-prominent
compounds are acoustically characterised by more or less level pitch and intensity across the
two constituents, while left-prominent compounds exhibit lower pitch and intensity on the
right element. These acoustic characteristics are in correspondence with theories that take
different compound prominence patterns to be realised by differences in the distribution of
pitch accents: in left-prominent compounds, only the first constituent is accented, while in
those with right prominence, there is a pitch accent on each constituent (cf. Gussenhoven
2004: 19, 276f., Kunter 2011: 66-67). When both constituents are accented, even if the second
accent is no higher than the first, the right-hand constituent is perceived as more prominent
because the expected declination in pitch does not occur. The question that we will
investigate in this paper thus boils down to whether the second noun will receive an accent
or not.
As we have seen, categorical rules are unsuccessful at predicting which of these
patterns of accentuation will apply to a particular NN compound. Recognising this, Kingdon
(1958: 149-152), for example, declines to give rules and states only that ‘one or two
indications can be given which will help in deciding on the correct stress’ (ibid.: 150). The
indications he gives, involving firstly semantics and secondly the possibility of contrast (an
indication of the relative informativeness of the constituents), have been recognised by many
authors before and since, and the ideas date back at least to Sweet (1892: 288-289). In fact,
8
there has been significant consensus amongst Anglicists for over a century about the factors
that influence compound prominence. The problem has been that most, if not all, of these
factors do not apply in a categorical fashion, and it was not until early in the twenty-first
century that alternative, non-categorical approaches were systematically applied to the
question. For example, Plag et al. (2007: 199-232) explicitly compared rule-based models of
compound prominence with two types of non-categorical model: logistic regression and
analogical. They found that both types of non-categorical model far outperformed the rulebased approaches in correctly predicting prominence.
The factors held responsible for the distribution of the two stress patterns are
numerous, and include semantics, argument structure, lexicalisation, constituent length,
constituent identity and informativeness. The recent large-scale studies by Plag and
colleagues (Plag 2006, 2010; Plag et al. 2007, 2008; Arndt-Lappe 2011; Kunter 2011) have
found substantial evidence for the influence of semantic factors, lexicalisation and
constituent identity, but empirical evidence for the role of informativeness is lacking. In this
paper we will fill this gap and investigate the role of informativeness, alongside other factors
that have already been shown to influence the assignment of compound prominence.
2.2 Informativeness
The idea that informativeness may play a role in compound stress assignment can be traced
back at least to Sweet (1892), who uses the term ‘logical prominence’ for what we call
‘informativeness’. He writes that the left prominence of compounds ’seems to be the result of
the second element being less logically prominent than the first, through being a word of
general meaning and frequent occurrence in compounds’ (ibid.: 288). Obviously, Sweet’s
reasoning rests on the still widely held assumption that, in general in language,
uninformative elements tend to be unaccented, while more informative and unexpected
information is accented. He identifies two ways in which a constituent can be relatively
9
uninformative: by ‘being a word of general meaning’, i.e. semantically non-specific, and by
occurring frequently in compounds, i.e. being relatively likely to be found in that position.
Several authors have followed in Sweet’s footsteps. Marchand (1969: 23) states that
‘The frequent occurrence of a word as second constituent is apt to give compound character
[left prominence] to combinations with such words’. Although he does not refer to the notion
of informativeness, the effect of a high positional frequency of a compound constituent can
be explained as an informativeness effect. The frequent occurrence of a word as second
constituent makes that constituent more predictable, and hence less informative, vis-à-vis the
other constituent, hence prone to be deaccented.
Jones (1972: 259) makes essentially the same point: ‘When the second element of a
compound is felt to be of special importance, double stress [right prominence] is used’.
Bolinger (1972) and Ladd (1984) suggest that this effect of informativeness can be understood
in terms of implicit contrast: so in silk SHIRT, for example, there is an implied contrast with
silk DRESS, silk TIE etc.
Despite the long existence of these perhaps intuitively plausible claims, the role of
informativeness in compound prominence assignment has, until recently, never been
empirically tested. However, there have been attempts to predict pitch accent placement in
general on the basis of measures of informativeness. In information theory, a standard
measure of ‘information content’ is the negative log likelihood of a word in a corpus
(Shannon 1948): the more frequent a word, and therefore the more likely, the less
information it is taken to convey. Pan & McKeown (1999) show that pitch accent placement
in texts can be quite successfully predicted on the basis of this variable: the less frequent a
word, and therefore the more informative, the more likely it is to be accented. They also
investigated another measure of informativeness, which was based on semantic specificity
and used the distribution of a word across different texts in a corpus. Again, the greater the
information content of a word, the more likely it was to receive an accent. Pan & Hirschberg
10
(2000) looked at the effect of local context on accent placement. They found that bigram
predictability, the probability of a particular word occurring in a context immediately
following the previous word, was a useful predictor of accentuation. The more likely a word,
given the occurrence of the previous word, the less likely it was to be accented. These studies
did not, however, address the particular issue of stress assignment in NN compounds.
Plag & Kunter (2010) is the only previously published study to have investigated the
effects of informativeness on compound prominence. As a measure of informativeness, they
used the positional family size of the constituents, i.e. the number of compound types in a
corpus that share a constituent in a given position. Each compound has a left constituent
family and a right constituent family. For example, the left constituent family of country house
would include compounds such as country club, country music and countryside, while the right
constituent family would feature compounds like town house, jailhouse and summer house. The
informativeness hypothesis can therefore be formulated in terms of family sizes: the larger
the positional family of N2, the more expected is N2 in this position and so the less
informative it will be. However, as well as family size, Plag & Kunter (ibid.) also took the socalled ‘constituent family bias’ into account (cf. Plag 2010), which is analogical in nature and
denotes the tendency for compounds with a particular word in N1 or N2 position to have the
same prominence pattern as other compounds with that word in the same position. In all
three of their corpora, they found only weak effects of family size, and these effects
interacted with the constituent family bias in such a way that for larger families the family
bias effect became stronger. The informativeness measure thus only acted as a modulator for
the much more significant family bias effect. However, their family sizes were based on quite
small corpora, and it is possible that more robust measures of informativeness would have
produced stronger effects.
In this study we use all three types of informativeness measure discussed above:
absolute predictability, relative predictability and semantic specificity. Given that N1 will be
11
accented irrespective of its informativeness, we focus our attention on the informativeness of
N2, either by itself, or in relation to the informativeness of N1.
Our first set of informativeness measures are based on the frequencies of the
constituents and of the compound. We will use a slightly simpler variant of Shannon’s (1948)
‘information content’, namely the frequency of N2. The more frequent the second
constituent, the less informative it is, and so our hypothesis is that the less likely it is to
receive stress. Our second measure expresses the relative informativeness of N2 vis-à-vis N1.
Mathematically, this is the conditional probability of N2 given N1, which can be expressed as
the compound’s frequency divided by the frequency of N1. This is the proportion of times
that, when N1 occurs, it is in the context of a compound with N2. The higher this conditional
probability, the less informative the second constituent will be and, according to our
hypothesis, the less likely it is to be accented.
We also include two conditional probability measures based on family sizes. The first
of these is simply the family size of N2, which is a measure of the probability of N2 occurring
as the second element in a NN compound. The second family size measure is the probability
of the occurrence of N2 given the family size of N1. If N1 has a large family, a particular N2
is less probable, and therefore more informative. Mathematically this conditional probability
based on family size can be expressed as 1 divided by the family size of N1. These two family
size measures are type-based measures, i.e. measures that count the number of different
compounds in which a constituent occurs. It would also be possible to use token frequency,
i.e. the summed frequencies of all such compounds. However, evidence from
psycholinguistics suggests that, in the case of compounds, the psychologically most salient
measure is the type frequency (Schreuder & Baayen 1997). We would therefore expect that, if
family sizes are predictive of compound prominence, the type frequency would be the best
predictor. Preliminary tests using both type-based and token-based family size measures
12
confirmed this: the type-based measures were indeed more highly predictive, and so we
used only type-based family size measures in our subsequent models.
Our final parameter of informativeness is semantic specificity. Semantically highlyspecific words can be considered more informative than words that have a less specific
meaning. In psycholinguistics, specificity of meaning has been operationalised with the help
of so-called ‘synsets’. This measure is based on the Wordnet lexical database (Fellbaum
1998), where a synset is a group of words with similar meanings: the greater the number of
synsets to which a word belongs, the more highly polysemous and the less specific in
meaning it is. Words with more restricted meanings are assumed to convey more specific
information, and therefore to be more informative, than words with a greater number of
meanings. Hence, we would expect right prominence to be less likely in compounds where
N2 has a high synset count, i.e. belongs to a large number of synsets, and is therefore
relatively non-specific. If relative informativeness is important, then we might also expect the
relationship between the synset count of N1 and the synset count of N2 to be significant: if
N1 is highly specific, we would predict that N2 will have to be even more specific to receive
an accent than if N1 were non-specific.
2.3 Semantics
The semantics of a compound has three elements: the meaning of the first noun (N1), the
meaning of the second noun (N2) and the relationship between them (R). For example, in the
case of table lamp, the relationship between TABLE and LAMP is that a table lamp is a lamp
designed to stand on a table. In the case of silk shirt, the relationship between SILK and SHIRT
is that a silk shirt is a shirt made of silk. Because R is not overtly expressed in the compound,
there is considerable scope for ambiguity and semantic opacity. A much quoted example
refers to compounds with oil, where olive oil denotes oil extracted from olives, but baby oil
13
refers to oil for rubbing on babies; similarly peach thing could denote something peach
coloured, made of peaches, or designed for holding peaches, amongst other possibilities.
There are many claims in the literature that right prominence in compounds is
determined by the semantics of the constituents or the semantic relation between the
constituents (see, for example, Plag et al. 2008 for a review of that literature). Large-scale
empirical studies have found that some of the predicted effects are indeed significant, but
they are probabilistic in nature, rather than being categorical rules. For example, Plag et al.
(2007, 2008) found the effects shown in table 1. There is some variation across the corpora
(CELEX, Baayen et al. 1995 and BURSC, Ostendorf et al. 1996):
Table 1: Semantic categories found to influence compound prominence (Plag et al. 2007,
2008)
influence
CELEX
BURSC
semantic property
N1 refers to a period or point in time
rightward

N2 is a geographical term
rightward

N1 and N2 form a proper noun
rightward


N1 is a proper noun
rightward


N1 and N2 form a left-headed compound
rightward

N1 has N2
rightward

N2 is made of N1
rightward


N1 is N2
rightward


N2 is located at N1
rightward

N2 occurs during N1
rightward

N2 is named after N1
rightward

N2 is for N1
leftward

N2 uses N1
leftward
semantic relation


14
For the present study, we include three of these relations that occur frequently in our data,
and which we define as N1 = TEMPORAL LOCATION DEFINING N2 (‘temporal’), N1 = SPATIAL
LOCATION DEFINING N2 (‘locative’), and N1 = MATERIAL OR INGREDIENT OF
N2 (‘made of’). We
also noticed that many of the compounds in this third group were names of food items,
which is a category mentioned by Gussenhoven & Broeders (1981) as favouring right stress.
We therefore decided to add this category to our classification: NN = NAME OF A FOOD ITEM
(‘name of food item’).
The fact that all four classes occur relatively frequently in the BNC data means that, if
we do not find a significant effect, we can be confident that this is not due to there being too
few examples of the categories.
2.4 Lexicalisation
Another influential factor is lexicalisation. According to many scholars, perhaps beginning
with Sweet (1892: 289), more highly lexicalised compounds seem to be more prone to left
prominence (see also Giegerich 2004 for a more recent claim in this direction). However,
lexicalisation is an intricate concept, and generally recognised to be not a categorical notion,
but rather a gradual one. Furthermore, there is no agreed test by which to decide whether a
given item is lexicalised, or, under a gradient view, more lexicalised or less lexicalised. In
general, the following criteria can be used: frequency, orthography, semantic transparency
and semantic institutionalisation. We discuss each briefly in turn.
It is generally assumed that lexicalisation strongly correlates with frequency (e.g.
Lipka 1994: 2165). The more frequent a word, the more likely it is to be listed in the mental
lexicon of speakers. It has also been shown that frequency correlates with orthography (e.g.
Plag et al. 2007, 2008). Compounds usually written as one word tend to have higher
frequencies than compounds usually written as two separate words, which is a strong
indication that orthographically concatenated compounds are more lexicalised on average
15
than non-concatenated, i.e. spaced, compounds. And concatenated compounds have indeed
been shown to have a very strong tendency to be left-stressed (e.g. Plag 2006, Plag et al. 2007,
Plag et al. 2008).
The orthography also relates to semantics, since we assume that writers tend to
concatenate those compounds that are felt to be a single unit instead of the sum of the two
individual parts. This is most clearly the case with semantically non-transparent compounds
such as butterfly. What exactly do we mean by non-transparency? In the case of compounds,
a compound is fully transparent if the meanings of both constituents are part of the meaning
of the compound. For example, a software company is a company that produces or sells
software, a honey bee is a bee that produces honey. These compounds are fully transparent,
since the meanings of the two constituents are part of the meaning of the compound as a
whole. This is different from butterfly ‘an insect with a long thin body and four usually
brightly coloured wings’ (Oxford Advanced Learner’s Dictionary 1995), where neither butter
nor flies are involved in the interpretation. One has to concede, however, that semantic
transparency is a gradient notion for at least two reasons. Firstly, there are at least three
degrees of transparency, depending on how many constituents’ meanings are part of the
meaning of the compound (none, one, or two). Secondly, the degree of similarity between the
constituent and its free form correspondent is also gradient. Thus one could argue that fly is
only partially opaque in butterfly, since a fly is also a flying insect. What all this boils down to
with regard to lexicalisation is the fact that a compound with an even partially opaque
meaning needs to be memorised with that meaning, and hence is lexicalised.
The final aspect of lexicalisation is what has been called ‘institutionalisation’. We
mean by this term that a potentially ambiguous compound takes on a particular meaning in
a given context or in the speech community at large. For example, a city hall is not just any
hall in a city but the building where the administration of the municipality is situated. In
such cases, particular meanings of the two constituents may still be part of the meaning of
16
the whole, but the compound is nevertheless memorised with a particular sense, and hence
is lexicalised.
To operationalise semantic opacity and institutionalisation one can use dictionaries.
In general, it can be assumed that dictionaries, for economic and practical reasons, tend to
list those complex words that are in some sense idiosyncratic, for example, have a meaning
that is not inferable from the constituent parts, or a particular meaning amongst several
theoretically possible ones. Hence, ‘listedness’, that is to say, having an entry in a dictionary,
can be taken as an indication that a compound is likely to be institutionalised or semantically
opaque. Of course, dictionaries also list some fully transparent complex words, but one can
assume that among those compounds listed in a dictionary there is a large proportion of
non-transparent ones.
In the light of these various considerations, the present study includes three variables
that are taken to be correlates of lexicalisation. These are listedness, compound frequency
and ‘spelling ratio’, which is the number of non-spaced tokens of a compound found in a
corpus divided by the number of spaced tokens, i.e. the ratio of non-spaced frequency to
spaced frequency. The prediction is that compounds that are listed, have a high frequency, or
a high spelling ratio will be more lexicalised and hence more prone to left stress.
2.5 Length of constituents
Another factor which may influence compound prominence is the length of the constituents.
Jespersen (1909: 153) remarks that right stress is often found with longer right constituents.
This factor has not been empirically tested in previous studies, and we include various
measures of length in the present analysis with a view to testing this prediction. These
include the number of syllables in N2, the total number of syllables in the compound, and
the number of syllables in the compound following the main-stressed syllable of N1. This
latter variable is a measure of the length of the unaccented ‘tail’ that would result from the
17
compound being left-stressed: it was included to follow up a suggestion by Ladd (1996: 244)
that there may be a cross-linguistic tendency for phonological constituents with left
accentuation to receive an additional accent on their final element when they are long (see
further discussion in section 4.2).
2.6 Identity of constituents
It has often been mentioned that certain compound constituents give rise to particular stress
patterns. A textbook example compares compounds with either street or avenue as the righthand constituent: these are predictably left-prominent in the case of street, e.g. PARK street,
and right-prominent in the case of avenue, e.g. park Avenue. In empirical studies of this effect,
constituent identity has turned out to be the most successful single predictor among all
known factors (Plag et al. 2007, Plag 2010, Arndt-Lappe 2011), no matter whether regression
models or computational analogical models have been used.
Plag (2010) used logistic regression modelling to investigate compound stress. He
operationalised the constituent identity effect as ‘constituent family bias’: for a given corpus,
this is the proportion of compounds with a shared constituent, in the same position, that
have a particular stress pattern. In other words, it is a measure of the probability that a
compound with a particular constituent in a given position will receive left or right
prominence. Because our dataset contains few compounds that share both left and right
constituents with other compounds in the data, we were unable to calculate meaningful
biases in this way. An alternative would have been to calculate biases on the basis of stress
patterns given in dictionaries. However, the compounds found in dictionaries tend to have a
great preponderance of left stress, corresponding to the high incidence of lexicalisation. In
the dictionary-based CELEX lexical database (Baayen et al. 1995), for example, over 90% of
the NN compounds have left stress. Dictionary data would therefore not be representative of
the kinds of compounds found in our data, where the incidence of left and right prominence
18
is much more balanced. In any case, it is interesting that, by incorporating informativeness
and constituent length, we were able to build a successful model without using constituent
identity. Further research will be needed to tease apart the relationship between these
various effects.
3. Methodology
3.1. Data
Previous empirical studies of compound prominence variability have either used
experimental data (e.g. Plag 2006), dictionary data (e.g. Plag et al. 2007), or speech data from
more specialised genres, such as the news text data from the Boston University Radio Speech
Corpus (BURSC) used by Plag et al. (2008). This paper extends the empirical scope to another
kind of data, namely compounds used in everyday conversation, whose stress patterns were
elicited in a controlled experimental procedure. This procedure is described below, and
further details, as well as the full dataset, can be found in Bell (2012).
The corpus from which the compound types were sampled is the British National
Corpus (BNC), a balanced corpus of 100 million words of British English dating between
1960 and 1994. This corpus consists of about 90 million words from written texts and 10
million from spoken texts, the latter being further subdivided into a contextual section
(speeches, radio broadcasts, lectures etc.) and a demographic section. The demographic
section is so called because it consists of 4.23 million words of spontaneous conversation,
recorded by informants selected to represent a demographically representative sample of the
population. This data was selected as a source of compounds that are actually used by
speakers in everyday conversations.
The compound types selected as experimental items all occurred in the corpus with
spaced orthography, i.e. transcribed as two orthographic words. Some of them also occurred,
elsewhere in the corpus, with hyphenated or concatenated orthography. By selecting
19
compounds that had at least one spaced token, it was hoped to get a balanced distribution of
left and right prominence: hyphenated and concatenated compounds are more likely to be
lexicalised and left-prominent, whereas spaced orthography occurs with both kinds of
prominence. Mark Davies kindly supplied a list of randomly ordered NN collocates
occurring in the demographic part of the BNC, representing a total of about 20,000 types.
Starting from the top of that random list, each collocate type was checked to establish
whether it was used as a compound in the corpus or not. Strings that were not compounds in
the sense defined in section 2.1, such as tea mother occurring in the context of Would you like
some tea mother? were excluded. Sampling ended after 1000 compounds had been found. At a
later point, we excluded those compounds from this list where either constituent was itself a
compound, since these did not conform to our definition of NN.
The prominence pattern of these compounds was established using a production
experiment, which is described in the next subsection.
3.2. Experimental setup
The compounds were presented visually on a computer screen to participants in the context
of a standard carrier sentence to be read out: She told me about the (compound). This sentence
was selected because it is sufficiently general for any compound to be inserted without the
sentence becoming meaningless, and also places the compound in final position, thus
eliminating the possibility of the compound prosody being influenced by a following accent
which might make it more difficult to perceive. Using the same carrier sentence for all items
was intended to minimise the possibility that differences in prosody between compounds
were due to differences in the contexts in which they occurred. Presenting the items in the
context of a sentence was also intended to reduce the risk that they would be read with a list
intonation which might affect prosody.
20
The presentations were produced as follows. To the 1000 sentences containing NN
compounds, were added 1000 similar sentences containing simplex nouns and 1000
containing AdjN combinations, also selected randomly from the demographic BNC, to be
used as fillers. This list of 3000 sentences was reproduced 4 times to give a total of 12,000
tokens. These were divided semi-randomly into lists of 300 sentences, so that each list
contained about 100 experimental (NN) items and no item appeared twice in the same list.
Lists were then assigned to participants, again semi-randomly, in such a way that no speaker
would repeat an item. In this way, four tokens of each item were elicited from four different
speakers, but each token appeared in a different list and position to guard against effects of
list position or reader fatigue. The sentences containing items or fillers were converted to
slides with one sentence per slide, and the slides presenting the items and fillers were
alternated with blank slides showing only a small cross. These blank slides were intended to
make the readers pause between reading successive items, again to guard against developing
list intonation.
The participants in the experiment were seventeen adult native speakers of educated
British English: eight with northern English accents, and nine with southern accents. Nine of
them were volunteers and eight were teachers of English as a Foreign Language who were
given remission from teaching in return for participation. The readers were told that they
were participating in a study of variation in pronunciation, and were asked to read the
sentences naturally, at their own speed, pausing between each one. The slides were
presented on a laptop computer, and readers used the keyboard to move on to successive
slides. No more than one list of 300 sentences was read at any session, and these were
presented in groups of 100 at a time, with readers taking a five minute break between
groups. Sessions were separated by at least one day. In this way, it was hoped to enable
readers to stay as natural as possible and avoid the reading becoming mechanical. Most
21
participants read 2 or 3 lists in total, but one person read only a single list and three people
read 5 lists each.
The readings were recorded onto magnetic tape using Sony Walkman professional
recorders and these recordings were subsequently digitised using Audacity sound editing
software. Using the same software, the compound tokens were then edited from the carrier
sentences.
After they had read their whole quota, participants were asked whether they were
aware of any rule about the assignment of prominence to NN combinations. One of the
teachers said she had been aware of such a rule, and so her data were excluded because of
the possibility that this belief had influenced her pronunciation. The items lost were
redistributed amongst other participants who had not already read them. Similarly, any
items where the reader stumbled, hesitated, repeated themselves or developed a ‘sing-song’,
list-like intonation were excluded and redistributed in subsequent sessions. Finally, because
of the random way items were assigned to lists, some ended up in contrastive contexts, for
example She told me about the oak tree immediately following She told me about the old tree.
Items in such contexts were excluded and reassigned. In this way, 4000 acceptable tokens
were elicited: four tokens, each from a different reader, for each of the 1000 types. The
number of NN compound tokens spoken by each participant ranged between 97 and 465
tokens, reflecting the different numbers of lists they had read.
3.3 Prominence rating
Not only is there variation in the prominence assigned by speakers to English compounds,
but there is also considerable variation in the way it is perceived: that is to say that one
listener might perceive a token as having right prominence, while a different listener, or even
the same listener on a different occasion, might perceive the same token as having left
prominence. Kunter (2010, 2011) showed that some listeners show greater inter-rater
22
agreement than others in this respect, and he was able to identify from his listeners a subgroup of relatively reliable raters, in the sense that they agreed with each other to a
statistically significant extent. For the purposes of the present data collection, two native
speakers of English participated in Kunter’s experiment and were found to be amongst the
reliable group. These raters subsequently produced three ratings for each of the compounds
in the experiment. The first rating was made online by the first rater while the participants
were reading the compounds, assigning either left or right prominence to each item that was
read out. To address potential concerns about the reliability of these online ratings, all tokens
were again rated by the first rater, as well as the second rater, using a special programme
developed by Kunter for his own experiment on the perception of compound prominence
(Kunter 2011).
In that experiment, Kunter (ibid.) presented compound tokens to his raters using a
graphical interface that included a slider bar with which they could indicate the relative
perceived prominence of left and right constituents, treating it as a continuous rather than
categorical variable. Moving the slider to the extreme left or right of the bar indicated
completely left or right prominence, while placing the slider at intermediate positions
indicated less contrast in the perceived prominence of the two constituents, and placing it
centrally indicated that they were perceived as equally prominent. His listeners could hear
items as many times as required before making a decision, and the programme then
converted the position of the slider into a number between 0 and 999, where extreme left = 0,
extreme right = 999 and centre point = 499. Kunter generously made his programme
available for the present data collection. Both raters used the programme to listen to all the
tokens in isolation, with the option of listening repeatedly rather than having to make an
instantaneous judgement. This meant that for each token there were three prominence
ratings, one categorical and two numerical. The numerical prominence ratings were
converted to categorical ones, with values of 0-499 indicating left prominence and values of
23
500-999 indicating right prominence, and the three ratings for each token were then
compared. In 97% of cases, the first rater’s two judgements were the same; of these, the
second rater’s judgement also concurred in 97% of cases, giving a total of 3764 tokens for
which all three judgements agreed. Those tokens for which there was not unanimity in the
ratings were excluded from further analysis.
3.4 Categories coded
In this section we describe the variables that we used as predictors in our models. We were
primarily interested in the effect of informativeness, but also included as covariates measures
related to effects found previously in the literature, i.e. semantic relations and lexicalisation,
and, additionally, constituent length. Table 2 summarises our predictors.
Table 2: Predictors initially present in the analysis
informativeness
semantic
relations
lexicalisation
length
frequency of N2
temporal
listed in dictionary
N2 syllables
conditional probability of N2
(based on N1 frequency)
locative
compound frequency
total syllables
made of
spelling ratio
syllables after
N1 main stress
family size of N2
conditional probability of N2
(based on N1 family size)
synset count N1
synset count N2
name of
food item
24
Informativeness
The frequency of N2 was taken from the whole 100 million words of the BNC using
the BYU-BNC interface (Davies 2004-). The frequencies used were lemmatised, which is to
say they included all inflectional variants of the word in question. To calculate the
conditional probability of N2 based on frequency, we also extracted lemmatised frequencies
for N1 and for the compound as a whole. Separate frequencies were obtained for the
compound written as two words (spaced) and one word (non-spaced), with hyphenated
tokens included in the non-spaced count. The compound frequency was then taken as the
sum of these two values, and conditional probability calculated as compound frequency
divided by frequency of N1.
For the predictors based on family size, we again used the BNC and the BYU-BNC
interface (ibid.). However, obtaining family size measures from a corpus presents special
challenges for the researcher. We first estimated family sizes from the whole BNC, by
searching for spaced noun-noun collocates in which the relevant values of N1 or N2 occurred
in first or second position respectively. Since, for the reasons mentioned in section 3.1, not all
collocates are compounds, we then had to take some measure to alleviate the danger of this
raw data giving us unrepresentative family sizes. The raw data would definitely overestimate family sizes: the important question was whether this overestimation was consistent
across compounds. Considerable care was taken to address this potential problem.
Initially, a semi-random sample of 187 families was selected, representing a balance
of N1 and N2 families from both left and right-prominent types. This sample included more
than 10 percent of all families in the data. For each of these families, all the constituent NN
collocates were checked to ascertain whether they actually occurred as compounds in the
corpus. Since this involved the checking of each and every token of each of the collocates,
assessment was restricted to the spoken corpus of the BNC, still 10 million words in size. The
accurate family sizes thus obtained were then regressed against the estimated family sizes.
25
There was a highly significant correlation (r=0.98, p<0.0001), but a number of outliers could
be identified, all of which showed estimated family sizes that were disproportionately large,
as against the (manually checked) actual family sizes. A closer look at these outliers revealed
that they belonged to the following categories: the constituents were vocatives in N2 position
(as in the example tea mother above); had homonymic forms (e.g. lead); were part of highfrequency formulae such as morning meaning good morning; had a high probability of being
mistagged, e.g. the post-nominal adjective present, as in highlights any special feature present; or
had very small family sizes.
In order to address the issue of outliers revealed by the sample, the complete set of
families was then searched for compounds potentially belonging to these five categories. 331
pertinent families were found. For all these additional families the actual family sizes were
also manually determined by checking all tokens in the spoken part of the BNC. In this way,
a sample of 518 families was established with both estimated raw counts for family sizes and
the actual family sizes (all based on the spoken part of the BNC). As before, the actual and
estimated family sizes for this sample were regressed onto each other, and again a very high
correlation (r=0.97, p<0.0001) was found. As expected, an inspection of the errors in the linear
model revealed a number of outliers: those with residuals larger than 1.5 standard deviations
were removed from the data to prevent them exerting undue influence on our statistical
models. After exclusion of these family size outliers we were left with 3252 tokens for our
analysis. For this set of data, it is safe to assume that the estimated family sizes are highly
correlated with the actual family sizes.
Synset counts were manually extracted from the Wordnet index file for nouns, for all
values of N1 and N2 in the data. In cases where a word did not appear in Wordnet (usually
because of dialectal differences between British and American English), the number of senses
was taken from OED Online. For three proper nouns, there was no entry in Wordnet or OED
Online: these cases were assumed to have one sense.
26
All these measures (constituent frequency, family size, and synset counts) are known
to influence the response times for visual lexical decision and word naming (Baayen 2005),
and it can be safely assumed that they have some psychological reality. For the present study
we use these measures to assess the effect of informativeness on compound prominence.
Semantic relations
Three annotators classified the compounds according to whether they belonged to any of the
four semantic categories identified in section 2.2, namely N1 = TEMPORAL LOCATION DEFINING
N2 (‘temporal’), N1 = SPATIAL LOCATION DEFINING N2 (‘locative’), N1 = MATERIAL OR
INGREDIENT OF N2 (‘made
of’), AND NN = NAME OF A FOOD ITEM (‘name of food item’).
Two of the annotators were native speakers of English, one also a trained linguist and
the other with an interest in linguistics; the third rater, although not a native speaker, had
native-like competence and a PhD in English linguistics. For each compound in the data set,
classifications were given by two of these three annotators; in cases where the two
judgements did not agree, the annotators discussed the classification to see if a consensus
could be reached. In the small number of cases where no consensus was possible, the items
were excluded from further analysis.
Lexicalisation
To check whether the compounds were listed, OED Online, the online version of the Oxford
English Dictionary, was manually searched for each one. There is considerable variation in
how compounds are listed in the dictionary, sometimes as full entries and sometimes under
one of their constituents, usually the modifier. Because of this inconsistency, any hit from the
main electronic search page (i.e. not including the full text) was counted as an entry.
As described above, lemmatised compound frequencies were taken from the whole
100 million words of the BNC using the BYU-BNC interface (Davies 2004-). Separate
27
frequencies were obtained for the compound written as two words (spaced) and one word
(non-spaced), with hyphenated tokens included in the non-spaced count. Compound
frequency was then defined as the sum of the two different spelling frequencies, while
spelling ratio was the non-spaced frequency divided by the spaced frequency.
In accordance with the previous literature, we take listedness, compound frequency
and spelling ratio to be primarily indications of semantic opacity. The assumptions are,
firstly, that combinations which are not semantically transparent are more likely to be listed
in dictionaries; secondly, that opaque compounds are on average more frequent than
transparent ones; thirdly, that concatenated or hyphenated orthography is used by speakers
when they perceive the two components of a compound as constituting a single conceptual
unit.
Length
Two native speakers of English determined the number of syllables in N1 and N2 for each
compound: any discrepancies were resolved by reference to the Longman Dictionary of
Contemporary English (1978). In cases with an optional schwa, this was included in the count:
for example, secretaries was recorded as 4 syllables rather than 3. The main-stressed syllable
of N1 was established in a similar way. The total number of syllables in the compound was
taken as the sum of N1 syllables and N2 syllables. The number of syllables after N1 main
stress was obtained by subtracting, from this total, the number of syllables in N1 up to and
including the syllable with main stress.
3.5 Statistical analysis
To alleviate the potentially harmful effects of extreme values on our statistical models, the
spelling ratio and all measures of informativeness were first logarithmatised.
28
For the statistical analysis, we used different kinds of multiple regression. In
regression analysis, the outcome of a dependent variable can be predicted on the basis of
independent variables. Multiple regression has the advantage of showing the effect of one
variable while holding all other variables constant, which is especially welcome for an
investigation like this one, where many different predictors seem to play a role at the same
time.
In addition to standard generalised linear models and mixed effects models (cf.
Baayen 2008), we also fitted generalised additive models (Hastie & Tibshirani 1990, Wood
2006), which provide a more precise way of modelling interactions involving two (or more)
numerical predictors, compared with standard linear models. The results of the additive
models were very similar to those of the standard linear models. Given that most readers
will be unfamiliar with the technicalities of generalised additive models, we decided to
report the results of the more straightforward standard models in this paper.2 The statistical
procedures will be explained in more detail as we go along.
In section 4, we will present the analysis of the full data set, which includes all tokens
for which the listeners agreed on the stress pattern. Since many compounds were accented
differently by different speakers, this dataset includes many compounds with both right and
left-stressed tokens, in varying proportions. Section 5 presents an analysis based only on the
compounds that were stressed in the same way by all four speakers, which are taken to be
those with the most nearly categorical prominence.
2
The smooth terms for the measures of informativeness in the generalised additive models sometimes
showed some wriggly, non-linear behaviour. A more detailed investigation of potential non-linearities
in the effects of informativeness measures remains a topic for future research.
29
4. Token-based analysis
4.1. Results
The full data set contains the 3252 tokens that remained after the computation of the family
sizes, and includes tokens of 864 of the original 1000 compounds. Table 3 gives an overview
of some properties of this data set.
Table 3: Data set for token analysis
number of tokens
3252
proportion of tokens with left stress
0.60
number of compound types
864
number of N1 lemma types
590
number of N2 lemma types
574
median family size of N1
221
median family size of N2
276
Of the 864 compound types in the data, 323 (37%) have at least one token with each of
the two stress patterns; in other words, 37% of the compounds show inter-speaker variation
in prominence. To address this variation, we used mixed effects regression modelling, which
allowed us to include speaker as a random effect (cf. Baayen 2008: 241ff.).
All the variables from table 2 were initially included as potential predictors. A closer
look at the numerical variables, however, revealed that a number of them were highly
correlated with one another. This collinearity is potentially harmful in regression analysis
since it means that different predictors compete in accounting for the same part of the
observed variation in the dependent variable. In order to reduce any negative impact on the
statistical models, the level of collinearity needs to be kept below a certain threshold, and so,
where predictors are highly correlated, it may be best to use only one of them.
30
In our data, the frequency of N2 and the family size of N2 were highly correlated.
Exploratory analyses showed that the predictive power of the family size measure was
greater, so we kept that measure and excluded the frequency of N2. Similarly, the
conditional probability of N2 based on N1 frequency (frequency of NN/frequency of N1)
was highly correlated with compound frequency. Here, our exploratory analyses revealed
that neither variable had a significant influence on prominence, and so we excluded them
both from further analysis. In the models that follow, ‘conditional probability of N2’
therefore refers to conditional probability based on N1 family size. Finally, all three measures
of length were correlated with one another. We found that, overall, the one that produced
the best and most interpretable models was the number of syllables after N1 main stress, and
we therefore kept just this one. Removing these correlated variables from the data resulted in
a satisfactory fall in collinearity, as indicated by a decrease in the condition number Κ from
58.3 to 18.0.3
We fitted a logistic generalised mixed effects regression model with speaker as a
random effect and with the other predictors as fixed effects. In addition to the individual
predictors, we also included an interaction term for the synsets, to see the combined effect of
N1 and N2 synsets: recall that if relative informativeness is important, then we might expect
this interaction to have a significant effect on prominence. We report the results of the final
model, from which all non-significant predictors were removed step-wise, following
standard procedures of model simplification.
We document the final model in table 4. The significant main effects for predictors
unrelated to informativeness are plotted in figure 1, the significant main effects for
informativeness predictors based on the probability of N2 are shown in figure 2, and the
significant interaction for informativeness predictors based on semantic specificity appears
3
The condition number Κ is used to assess the danger of collinearity in the data. According to Baayen
(2008:182), condition numbers of 30 and more may indicate potentially harmful collinearity.
31
in figure 3. For the categorical variables, the dots indicate the mean estimated probability of
N2 receiving an accent, in compounds that have the pertinent value of that variable. For the
continuous variables, the graphs show regression lines. To show the effect of each predictor
in turn, the other predictors are adjusted to their reference level (for categorical variables) or
to their means (for continuous predictors). The reference level for the categorical variables is
‘no’: in other words, the model shows the effect of independently varying each predictor in a
situation where none of the (other) semantic categories applies and the compound is not
listed (except where this is the predictor in question). In the table, positive coefficients
indicate a tendency towards right prominence, negative coefficients towards left
prominence. The model is quite successful in its predictions (C=0.846)4, in spite of the fact
that so many compound types are variably stressed in this dataset.
4
C is the concordance index, and it measures the concordance between the outcome predicted by the
model and the value actually observed. A C-value of 0.5 indicates total randomness, 1 indicates
perfect predictions. A C-value of below 0.8 indicates that the model is not very useful in making
predictions because of the high error rate (cf. Kutner et al. 2005).
32
Table 4: Mixed effects regression model, final token-based analysis, N=3252, C = 0.846
Random effect:
Variance
Std.Dev.
0.23177
0.48143
Estimate
Std.Error
z value
Pr(>|z|)
Sig.
(intercept)
-1.54510
0.34227
-4.514
6.35e-06
***
temporal
2.59219
0.28451
9.111
< 2e-16
***
locative
1.81599
0.15704
11.564
< 2e-16
***
made of
2.71829
0.14471
18.785
< 2e-16
***
listed in dictionary
-0.74772
0.09992
-7.483
7.25e-14
***
log spelling ratio
-0.28695
0.03047
-9.419
< 2e-16
***
0.34126
0.04692
7.272
3.53e-13
***
log family size of N2
-0.27984
0.04293
-6.518
7.11e-11
***
log conditional probability of N2
-0.09968
0.04033
-2.472
0.0134
*
log synset count N1
0.59942
0.14180
4.227
2.37e-05
***
log synset count N2
0.01310
0.11872
0.110
0.9122
log synset count N1 : log synset count N2
0.20115
0.08493
-2.368
0.0179
speaker
Fixed effects:
syllables after N1 main stress
Significance codes:
***
**
*
*
p< 0.001
p< 0.01
p< 0.05
We will first discuss the measures that are not related to informativeness. Their
effects are illustrated in figure 1. Three of the four semantic relations show a significant main
effect in the expected direction. The positive coefficients for these predictors mean that
compounds expressing a ‘locative’, ‘temporal’ or ‘made-of’ relation have a significantly
higher chance than other compounds of having an accent on N2. The fourth category (‘name
of food item’) was non-significant in the full model (p = 0.642). This may have been because
the overwhelming majority of ‘food item’ compounds also fell into the larger ‘made of’ class,
which could therefore have subsumed any independent effect of the smaller category. We
also see that both predictors based on lexicalisation work in the expected direction. Firstly, if
a compound is listed it has a greater chance of being left-stressed. Secondly, the higher the
spelling ratio, i.e. the greater the proportion of a compound’s tokens that are written non-
33
spaced, the lower is the chance that N2 will be accented. Finally, there is a main effect of
length, such that the more syllables there are in the compound after the main-stressed
1.0
0.8
0.6
0.4
0.2
0.0
no
probability of accent on N2
probability of accent on N2
probability of accent on N2
syllable of N1, the greater is the chance that N2 will be accented.
1.0
0.8
0.6
0.4
0.2
0.0
yes
no
0.6
0.4
0.2
0.0
no
yes
1.0
0.8
0.6
0.4
0.2
0.0
yes
listed in dictionary
made of
probability of accent on N2
locative
probability of accent on N2
probability of accent on N2
0.8
yes
temporal
no
1.0
1.0
0.8
0.6
0.4
0.2
0.0
-8
-4
0
2 4 6
log spelling ratio
1.0
0.8
0.6
0.4
0.2
0.0
1
2
3
4
5
6
syllables after N1 main stress
Figure 1: Partial effects in the final mixed model, predictors unrelated to informativeness
Let us now turn to those predictors that measure informativeness. As we can see from
table 4, both probability-based measures turned out to be significant. The plot for the family
size of N2, given in the left-hand panel of figure 2, shows an effect in the expected direction.
The larger the family size, i.e. the less informative N2 is, the less likely it is to be accented.
The right-hand panel of figure 2 shows the expected effect for conditional probability. The
greater the likelihood of N2, given N1, the lower is the probability of an accent on N2.
34
1.0
probability of accent on N2
probability of accent on N2
1.0
0.8
0.6
0.4
0.2
0.0
0.8
0.6
0.4
0.2
0.0
0
2
4
6
8
-8
log family size of N2
-6
-4
-2
0
log conditional probability of N2
Figure 2: Partial effects in the final mixed model, informativeness predictors based on
probability of N2
There is also a significant interaction term for the synset counts. In order to
understand this interaction it is useful to inspect the contour plot, shown in figure 3. The
contour plot is three-dimensional in nature and works in analogy to topographic maps, with
valleys and mountains indicating opposing values of the dependent variable. The x and y
axes show the values of the two predictors. The contour lines indicate regions with the same
probability of right stress, with the probability of accent on N2 given as a figure on the line.
The contour plot shows a growing probability of stress on N2 for compounds towards the
lower right corner of the plot. This means that with non-specific N1’s and highly specific
N2’s, we find the highest probability of N2 being accented. This is fully in accordance with
the hypothesis: accentuation of N2, and therefore right prominence, is most likely when N2
is highly informative relative to N1. We also see that the effect of N2 specificity only kicks in
beyond a certain threshold of N1 non-specificity. Thus, for the most specific N1’s the
specificity of N2 does not have a strong influence on stress placement. Similarly, for the least
specific N2’s, the specificity of N1 has little effect.
2.0
0.0
1.0
0.3
5
0.4
0.3
0.2
0.2
5
1.0
0.0
log synset count N2
3.0
35
5
0.
2.0
3.0
log synset count N1
Figure 3: Partial effects in the final mixed model, informativeness predictors based on
semantic specificity (contour lines indicate probability of accent on N2)
To summarise the results, we find clear effects of informativeness that are in line with
the predictions, with less informative second constituents being less likely to be accented.
Beside the informativeness of N2 itself, as indicated by its family size, the informativeness of
N2 relative to N1 also has a say in stress placement, as shown by the effects of conditional
probability and the synset interaction.
4.2. Discussion
With regard to established determinants of compound stress assignment, we found the same
effects as previous studies. Our findings confirm the role of semantic relations and
lexicalisation as determinants of prominence.
We also found a new effect, that of length. Why should longer ‘tails’, i.e. longer
strings of syllables following the first main stress, attract prominence to N2? Ladd (1996: 244)
suggests that there may be a cross-linguistic tendency for phonological constituents with left
accentuation to receive an additional accent on their final element when they are long. He
36
explains this in terms of a metrical theory of sentence stress, whereby longer sentences are
composed of more than one intermediate phonological phrase, each of which carries an
accent (Ladd 1996: 275-276). Given that phonetic studies of compound prominence (Kunter
2011: 1-261) indicate that ‘right’ prominence is more accurately described as double
accentuation, Ladd’s analysis can easily be extended to NN compounds: compounds of
sufficient length constitute two intermediate phonological units. However, the motivation
for such a metrical structure is not entirely clear. Ladd states that ‘when … sentences are
longer, it is difficult to treat the whole sentence as a single intermediate phrase’, but does not
specify where this difficulty lies. Nevertheless, we might speculate that the difficulty, if it
exists, is related to the mechanics of speech production.
In addition to the effects just discussed, we found robust effects of informativeness on
compound prominence assignment. Recall that left-prominent compounds carry one accent,
while right-prominent compounds carry two, one on each of the constituents. Hence,
placement of an accent on the first constituent is a given, and the issue is whether the second
constituent also receives an accent. According to the hypothesis, the decision about placing
this accent would naturally involve assessing primarily the informativeness of the entity that
is to receive that accent (or not), i.e. the informativeness of N2. The regression models show
effects in the predicted direction. More informative second constituents attract stress,
whether informativeness is measured in terms of probability, relative probability or semantic
specificity.
The robust effects of informativeness on accent placement in NN compounds are in
line with the findings by Pan & McKeown (1999) and Pan & Hirschberg (2000). These
authors quite successfully employed word frequency measures and the conditional
probability of the second word in a two-word sequence to predict accentuation in medical
texts. As in this study, more informative constituents were more likely to receive an accent.
37
Our results differ from those of Plag & Kunter (2010). These authors found that, in
their corpora, family size only added to the much more significant effect of family bias. One
probable reason for the discrepancies between their findings and ours could be that their
family sizes were based only on dataset-internal families, hence very small, while our family
sizes are derived from a very large corpus, hence very large, and thus potentially more
highly correlated with actual family sizes. A more important difference between the two
studies, however, is that we included different, and more, informativeness measures in this
study, whereas Plag & Kunter (2010) used only one, namely family size. The fact that a
variety of measures of informativeness reach significance in the present models lends further
support to the idea that informativeness is a powerful determinant of compound
prominence.
5. Type-based analysis: compounds without inter-speaker variation in stress
5.1. Results
Of the 864 compound types in our data, 541 types were accented in the same way by all four
speakers. Of these, 341 were consistently left-prominent, and 200 were consistently rightprominent. The analysis presented in this section uses only these non-variable compounds.
Because there is no inter-speaker variation in this dataset, we did not need to include speaker
as a random effect, and were therefore able to use standard logistic regression rather than
mixed effects modelling. In the statistical programme R, logistic regression modelling has the
advantage that there are readily available procedures for model criticism, which help to
increase confidence in the resulting models.
The results of a logistic regression analysis of this data set were very similar to the
results described in the previous section. In the full model, we initially included the same
predictors as in the token-based analysis, but without the random effect for speaker. The full
model, using all predictors, was simplified in the usual fashion by stepwise elimination of
38
non-significant predictors. The resulting model contained the same predictors as the final
token-based mixed model described above, except for the random effect of speaker. To check
that the model did not over-fit the data, we used bootstrap validation (with 200 bootstrap
runs in the simulation), which is a way of detecting and eliminating any superfluous
predictors in a model: none of the predictors were eliminated during this process. To
ameliorate the effects on the model of any extreme values in our dataset, and thereby
increase the model’s predictive power for new data, we then applied penalised maximum
likelihood estimation (cf. Baayen 2008: 225). This involves introducing a penalty factor into
the model to guard against over-large coefficients, i.e. to avoid exaggerating the effect of any
of the predictors.
The final penalised model is shown in table 5. Again, positive coefficients indicate a
tendency towards right prominence, negative coefficients towards left prominence. Notably,
we now find a higher C-value of 0.918, which means that this model is even better in its
predictions than the token-based analysis. This is not unexpected, since the token-based
model includes within-type variation whereas the present analysis focuses on those types
that show no inter-speaker variation in our data. After penalisation, the effect of the synset
count interaction is now only marginally significant, but it does survive subsequent
validation of the penalised model. The graphs for the type-based model are extremely
similar to the ones shown in figures 1-3 for the token-based model and are therefore not
reproduced here. To summarise, the analysis of the non-variable compounds further
substantiates the contribution of informativeness to compound stress assignment as already
found in the token analysis.
39
Table 5: Logistic regression model, final type-based analysis, N=541, C = 0.918
Coef
S.E.
Wald Z
P
Penalty
Scale
Sig.
intercept
-3.0992
0.98508
-3.15
0.0017
0.0000
**
temporal
3.7436
0.90451
4.14
< 0.0001
0.3162
***
locative
2.5597
0.46334
5.52
< 0.0001
0.3162
***
made of
3.7208
0.42326
8.79
< 0.0001
0.3162
***
listed in dictionary
-0.9263
0.29258
-3.17
0.0015
0.3162
**
log spelling ratio
-0.4145
0.08636
-4.80
< 0.0001
0.8409
***
0.4490
0.13622
3.30
0.0010
0.4440
**
log family size of N2
-0.3919
0.12943
-3.03
0.0025
0.6246
**
log conditional probability of N2
-0.3181
0.12521
-2.54
0.0111
0.5641
*
log synset count N1
1.0600
0.40764
2.60
0.0093
0.3376
**
log synset count N2
0.1198
0.35253
0.34
0.7339
0.3518
-0.4812
0.24706
-1.95
0.0515
0.6905
syllables after N1 main stress
log synset count N1 : log synset
count N2
Significance codes:
***
**
*
•
•
p< 0.001
p< 0.01
p< 0.05
marginal
5.2. Discussion
It is interesting to compare the success of our models with those previously published in the
literature. Plag (2010) used regression analysis to model compound prominence on the basis
of semantic variables, lexicalisation and constituent bias, as defined above in section 2.6.
Table 7 shows a comparison between his results and those presented here. The statistic C
gives an indication of the models’ success in predicting stress: the higher the C value, within
the range 0.5 – 1.0, the greater a model’s success. Overall, it can be seen that models that
include information about length and informativeness perform at least as well as those that
have information about constituent bias but not about informativeness or length.
40
Table 6: Comparison of model fits across different regression analyses
(BURSC and CELEX models from Plag 2010)
predictors in analysis
corpus
data
C
informativeness
semantics
lexicalisation
length
constituent
bias
BNC
types
0.918




BNC
tokens
0.846




BURSC
types
0.794



BURSC
tokens
0.828



CELEX
types
0.899



To look more closely at the predictive accuracy of some of these different models, and
also to compare them with published analyses using analogical modelling, we calculated the
probability of right stress, as predicted by our type-based model, for each of the 541
compounds in the non-variable dataset. These probabilities were then converted into
categorical predictions, left or right, with all probabilities below 0.5 counting as left, all
others as right. This allows us to directly compare the model’s predictions with the observed
stresses, as shown in table 7.
Table 7: Predicted vs. observed stress, final type-based model
observed left
observed right
predicted left predicted right
316
25
44
156
From this information, it is possible to calculate the proportion of compounds in the data for
which the model predicts the attested stress pattern. It is also possible to analyse the
proportion of correct predictions for right-stressed and left-stressed compounds separately.
Table 8 compares the predictive accuracy of different type-based models: the logistic
regression models presented here and in Plag (2010) as well as analogical models described
in Arndt-Lappe (2011), which are based on the same databases as Plag (2010) and use the
41
computational algorithm AM::Parallel (Skousen et al. 2004). Looking first at the two models
based on the CELEX lexical database (Baayen et al. 1995), we see that they are extremely
successful overall, with 95% success for both regression analysis and analogical modelling.
However, a look at the left-stressed and right-stressed compounds separately, shows that,
although left stress can be predicted with 99% accuracy, the predictive accuracy for right
stress is far below chance. In other words, both the regression analysis and the analogical
model far over-predict left stress for this data: the high predictive accuracy overall is
explained by the fact that 94% of compounds in the data are left-stressed.
Table 8: Comparison of predictive accuracy across different type-based models (BURSC and
CELEX regression models from Plag 2010; analogical models from Arndt-Lappe 2011)
approach
regression
analysis
analogical
modelling
proportion
left stress
predictive
accuracy for
left stress
predictive
accuracy for
right stress
predictive
accuracy
overall
BNC
0.63
0.93
0.78
0.87
BURSC
0.67
0.92
0.50
0.78
CELEX
0.94
0.99
0.32
0.95
BURSC
0.67
0.90
0.61
0.80
CELEX
0.94
1.0
0.20
0.95
corpus
It can be seen from table 8 that, in terms of the proportion of left stresses, the BNC
data used here is much more similar to the Boston Radio Speech Corpus (BURSC, Ostendorf
et al. 1996) than it is to CELEX. Comparing the BURSC models with our BNC model we see
that, overall, the BNC model using informativeness and length is somewhat more successful
than either of the models using constituent bias. However, when we look at the figures for
left stress and right stress separately, we see that all three models are actually very similar in
terms of their ability to predict left stress, but the present model is much more successful at
predicting right stress. It therefore seems that including information about the
42
informativeness of N2, and the number of syllables after the main-stressed syllable of N1,
significantly increases the power of probabilistic models to predict right stress. This can be
understood in terms of accentuation: if N2 is more highly informative, then it is more likely
to be accented, i.e. the compound is more likely to be right-stressed. Similarly, if left stress
would produce a long, unaccented string of syllables, then a second accent, i.e. right stress, is
more likely.
6. Conclusion
In this paper, we have investigated the question of whether informativeness is a determinant
of compound prominence in English. Analyses both of compounds showing within-type
variation in prominence, and of those showing no within-type variation in our data, have
provided very robust evidence for an effect of informativeness on stress assignment. In
accordance with the predictions, it has been shown that all measures of informativeness can
help to predict the probability of a compound having a particular stress pattern. In general,
the more informative N2 is, the more likely it is to receive an accent; in other words, the more
informative is N2, the more likely is the compound to be right-stressed. Predictably, this
effect can be modulated by the informativeness of N1, as shown by the significant interaction
effect of the synset counts and by the effect of the conditional probability of N2. Given the
significance of the family-based measures of informativeness, our results also substantiate
the role of constituent families in compound structure and processing. Constituent families
have been shown to be influential not only in stress assignment (e.g. Plag 2010, Arndt-Lappe
2011), but also in compound semantics (e.g. Gagné & Shoben, 1997; Gagné, 2001) and
compound morphology (e.g. Krott et al. 2002, 2007).
The effect of informativeness has repercussions for the phonological analysis of
compound prominence. As mentioned in the introduction, we follow an analysis of
compound prominence in terms of pitch accent placement, instead of lexical stress. In this
43
view, the role of informativeness is to be expected and is naturally accounted for:
uninformative elements do not receive a pitch accent. In an approach where compound
stress assignment is a lexical-phonological process, the role of informativeness is unexpected
and seems inexplicable. Our results therefore provide independent evidence for an accentbased theory of compound prominence.
We also found, for the first time, empirical evidence for a length effect. The greater
the number of syllables after the main-stressed syllable of N1, the higher the chances of the
compound bearing two accents and hence of N2 being prominent. Finally, in addition to
these new effects, we found the same effects of semantic relations and lexicalisation as
reported from previous studies. The reader should note that all effects were measured while
controlling for the effects of other predictors, which means that we have good evidence that
they are all independent determinants of compound prominence.
The semantic effects and the lexicalisation effect largely replicate those found by Plag
and colleagues for other corpora (e.g. Plag et al. 2007, 2008). The informativeness effect is
unprecedented in compound research, but in line with the studies by Pan and colleagues
mentioned above (Pan & McKeown 1999; Pan & Hirschberg 2000), who found general effects
of informativeness on accent placement in texts. Our results differ, however, from the results
of Plag & Kunter (2010). They found that the most significant predictor of prominence is the
family bias, that is to say the tendency for compounds that share the same first or second
constituent to receive the same kind of prominence. Although we did not include this
variable in our models, it is to be expected that family bias effects would be found in our
data, since any predictor that is specific to N1 or N2 will inevitably give rise to such an effect.
For example, all compounds with the same second constituent will also have the same
number of syllables, frequency, synset count and positional family size for N2. If long
constituents in N2 position increase the chance that N2 receives an accent, then longer words
in N2 position will tend to have greater bias for right stress than shorter words. Similarly, if
44
informative constituents in N2 position are more likely to be accented than less informative
constituents, then more informative words in N2 position will tend to have greater bias for
right stress than less informative words. Thus it is even conceivable that length and
informativeness measures could underlie the family bias effect. This in turn would explain
why the results of models that include family bias, but not length or informativeness, are
similar to the results presented here. The main difference between our results and previously
published models of compound prominence is that our models are more successful at
predicting right prominence. However, because we used a different corpus, we cannot be
sure that this difference is not an artefact of the data. Clearly, what is called for is a study that
includes both informativeness measurements and family bias in order to tease these effects
apart.
A further question for future research concerns the variation in prominence between
different tokens of the same compound. Very little is known about what influences the extent
of this variation, although some compounds appear to be more variable than others (cf.
Kunter 2011, chapter 8). However, the involvement of constituent informativeness in
prominence assignment suggests that this might also contribute to variability. The family
sizes of constituents will vary across the mental lexicons of different speakers, and
unconscious perceptions of informativeness might also therefore differ. In general, however,
one might expect more variability where N2 is neither very informative nor very
uninformative, either in absolute terms or relative to N1. Furthermore, variability might arise
when informativeness conflicts with some other determinant of prominence: for example,
when a compound has a semantic relation that predisposes it to right stress, but a very
uninformative right-hand constituent. Initial results (Bell & Plag, in preparation) suggest that
such mechanisms might indeed be involved.
45
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